Desert
Volume:27 Issue: 2, Summer -Autumn 2022

Environmental and human health requirements necessitate replacing chemical herbicides with natural ones for weed control. Allelopathy is a form of interaction among plants that may assist in producing natural herbicides, but less research has been conducted in this important area. Allelopathic effects of extracts of five drought-tolerant plant species widely used in urban landscapes were examined using five completely randomized design experiments with four replications. The species were Rosmarinus officinalis, Lavandula officinalis, Salvia sclarea, Atriplex halimus, and Atriplex canescens. The plant species' soluble extracts were prepared at 0, 2.5, 5, and 7.5%. The seeds' germination percentage and rates of Taraxacum officinale, Conyza canadensis, and Tragopogon major were measured daily after being affected by these extracts. Increasing the concentrations of all the extract types was associated with germination percentages and rate declines. The most outstanding allelopathic effects were related to Atriplex halimus and Salvia sclarea extracts, respectively. Tragopogon major was considered the most sensitive weed species in reducing its germination rate when affected by the landscape plant extracts, except for R. officinalis extract. The reduction in germination percentage of T. major and C. canadensis under A. halimus extract compared to the control was the highest. A. halimus and S. sclarea extracts can be used as natural weed controllers for important weeds such as Taraxacum officinale.

The study of heavy metals in dust fall is very important due to effects on human health. The purpose of the present study has been to determine the level of contamination and ecological risk of heavy metals such as Cd, Cr, Cu, Ni, Pb in the falling dust of Tehran city, and investigation spatial distribution of pollution on the studied stations. Dust fallout samples were collected using Marble Dust Collector (MDCO) from 28 different locations across the Tehran city, during the statistical period (from December 22nd, 2017 to June 21st, 2018). Contamination factor(CF), pollution Load index(PLI), The potential ecological risk coefficient(Er) and The potential toxicity response index(RI) were used to identify the level of contamination and ecological risk of heavy metals. The amount of (Cf), (PLI), (Er) and(RI) for the heavy metals in the dust fall in winter and spring 2018 followed the order of Pb>Cd>Cu>Cr>Ni. The concentrations of Lead, Copper and Cadmium in winter were significantly higher than those in spring. Stable air, temperature inversions and more heating devices are used in winter, causes that heavy metals are increased in this season. Areas located in the east of Tehran have the highest pollution and ecological risk in terms of cadmium, copper, nickel and lead. Most of the chromium contamination exists from the central areas to south of Tehran. Tehran's prevailing wind direction and Tehran's topographic pattern, mines, factories and industries located in the west and southwest of Tehran have main role in polluting Tehran's falling dust with heavy metals.

Arid ecosystems have a high capacity for carbon pool since they involve 41% of the world’s land surface. It is important to make reliable estimations of the amount of carbon stored in the soil and plat of rangelands. The present study was conducted to compare the ability of two native plant species, namely Artemisia sieberi Besser. and Zygophyllum atriplicoides Fisch. & C.A.Mey., concerning carbon pool in arid rangelands of Luchunasi, Sistan and Baluchestan province, Iran. The data were collected in 2020 through a randomized complete block design. We measured soil bulk density, organic carbon, pH, EC, and soil carbon pool from three soil layers (0–30, 30–60, and 60–90 cm), as well as carbon pool of aboveground and belowground biomass. The data were analyzed via analysis of ‎variance and paired T-test. The obtained results indicated that in both habitats, the maximum levels of soil carbon pool, bulk density, and organic carbon were observed in the 0-30-cm soil layer. In Z. atriplicoides habitat, Cp in the depth of 0-30 cm was higher than that in A. sieberi habitat. In both plants, Cp in the belowground biomass was significantly higher than the aboveground parts (P<0.01). Moreover, our study showed that Z. atriplicoides (shrub form) has further potential to store carbon compared with A. sieberi. (bush form). The use of plants with shrub form in biological practices can increase the carbon pool in arid lands, but the efficiency of more plant species needs to be assessed.

Land subsidence has caused severe environmental hazards in most plains of Iran due to unbalanced extraction between groundwater and rainfall and the geodetic factors. In this regard, three basins of Kohpaye Segzi, Isfahan Borkhar, and Najafabad in Isfahan province were selected to study the areas with land subsidence vulnerability. Changes in aquifer water volume influenced by geodetic factors and meteorological drought were studied. The maps of the Standardized Water Level Index (SWI) and isodeep were provided in ArcGIS 10.7 software using the statistical data of piezometric wells (2002-2018). The time series analysis of 6, 12, 18, and 24-month were performed by DIP software for September as the driest month of the year. the time series with the highest correlation was zoned to show the number of SPI changes. In the last step, the weight of all indices including groundwater loss, meteorological drought, slope, and altitude was equated to evaluate land subsidence vulnerability. Land subsidence vulnerability map was prepared by overlaying the fuzzy maps of indices with strategic points. The relationship between meteorological drought and groundwater level; and correlation analysis of these two parameters with the Pearson statistical method showed a positive correlation only in 18-months time series. The results also showed that 4202 of the region has located in high to very high drop class, and the groundwater table has decreased 9.05 m from 2002 to 2018. In general, with a negative trend of precipitation, a positive trend was observed in the standard water level index, which increases the effective stress and was the main reason for land subsidence. According to the vulnerability map, 49 and 12.5 percent of the study area were categorized into high and very high classes of landslide vulnerability, respectively. The results showed that the probability of land subsidence will increase in the next few years because of reduction in precipitation due to climate fluctuation, slope effects, altitude, over-harvesting of groundwater potential in all parts of the basin, especially in the northern areas, and increasing density and loading especially in recent years.

Species interactions are considered important in the process of understanding the structure and composition of natural plant communities. This study evaluated a field survey to investigate species composition and interspecific associations in natural vegetation of an arid area in Hanitie rangeland, Khuzestan Province, Southwest of Iran. The interspecific associations of major species were quantitatively analyzed using a 2 - 2 contingency table, χ2  test, and interspecific association coefficients. Eighty quadrates were located along four 800m transverse transects, two of which were located in the general direction of dominant winds while others were perpendicular to the dominant wind. This vegetation type's quadrate size was determined using the minimal area method (3 3m). Thirty-eight species belonging to 10 families were recorded on this site. The highest number of species belonged to the Graminea, followed by  Chenopodiacea. This study shows that Holocnemum strobilaceum and Aeluropus littoralis have identical interspecific association patterns and share a positive interspecific association. But the distribution of Tamarix leptopetala was significantly different (P<0.05) within Aeluropus littoralis and Agropyron elangatum, which indicates a negative association. Also, the results show that when Holocnemum strobilaceum and Aeluropus littoralis co-occurred in the general direction of dominant winds, the mean frequency was significantly higher than when they co-occurred perpendicular to the dominant wind. This research may support the view that facilitation is more prominent in a severely disturbed habitat than the competition

Estimation of actual evapotranspiration (ET) in large areas is an important part of water resources management. In recent years, remote sensing has been successfully used in ET estimation, which is supposed to be more accurate for estimating ET on regional and agricultural scales. The main aim of this investigation is to evaluate the efficiency of two algorithms namely Surface Energy Balance Algorithms for Land (SEBAL) and Mapping ET at high Resolution with Internalized Calibration (METRIC) algorithms for estimating actual ET from agricultural lands in Davarsen County, Iran. Accordingly, six Landsat 8 OLI/TIR satellite images and Lysimeter data installed in these lands were used. The amounts of actual ET were estimated using two algorithms and the obtained results were compared with Lysimeter data. Based on the results of evaluation, Root Mean Square Error (RMSE) of 0.54 and 0.64 mm day-1, Nash-Sutcliffe Efficiency (NSE) criteria of  0.85 and 0.79, Mean Bias Error (MBE) of 0.04 and 0.02 mm day-1, Mean Absolute Error (MAE) of 0.42 and 0.48 mm day-1 and coefficient of determination  (R2) of 0.86 and 0.82 were estimated for SEBAL and METRIC algorithms, respectively. These statistical indices show that these algorithms have a high accuracy for estimating actual ET in the study area. The executive applications of this study can be used to determine the exact amount of evapotranspiration in irrigated lands for water allocation planning, optimization of crop production, irrigation management and assessment of land use change on water efficiency.

Land degradation is a global natural hazard that can be controlled by distinguishing susceptible areas. Although new approaches for determining areas prone to land degradation are necessary, spatial modeling of this hazard remains a challenge. This study aimed to investigate the efficiency of the weight of evidence (WOE) and evidential belief function (EBF) models for spatial modeling of land degradation in a semi-arid region in Iran. The trend of Net Primary Production (NPP) changes related to 2001-2020, obtained from MOD17A3, was taken into account to specify the inventory of land degradation in the study area. 120 random points were chosen as degraded points in areas with decreasing trend in NPP during 20 years. 70% of the dataset was randomly selected as a training set for the modeling step and 30% of them were selected as the testing set for the validation step. Fifteen geo-environmental factors including temperature, precipitation, slope, aspect, altitude, land use, normalized difference vegetation index, normalized difference salinity index, vegetation soil salinity index, normalized difference moisture index, visible and shortwave infrared drought index, electrical conductivity, and sodium adsorption ratio of groundwater, groundwater table, and annual depletion of groundwater resources were selected as influential factors or independent variables for modeling. The modeling process was done in ArcGIS software after calculating the values of EBF and WOE in excel. And finally, the efficiency of the models was analyzed using the area under the ROC curve. The findings illustrated that EBF with AUC = 0.72 had better performance for spatial modeling of land degradation in the Qazvin plain. Also according to the outputs of both models, north, northeast, northwest, west, southwest, and south of the Qazvin plain were susceptible to LD. The results of this research successfully suggested a new land degradation modeling method that can be used in different areas.

Climate change is one of the most pressing problems among scientists worldwide, with experts warning about it and even referring to it as unfathomable human agony. In this study, we reviewed previous studies and examined two gaps in the existing approach to climate change studies. First, look at the "side effects" of global warming that have been overlooked in the process and then look at the leading "cause" of global warming, namely "humans" and not its "effects". The findings revealed that a 1.4 °C temperature increase (as predicted by United National (UN) projections) would not only raise this amount but also trigger further global warming. As a result, the premise that global warming produces additional global warming was proven. In the Water Area (WA) class, radiant energy increased by 1194.8%, compared to 1205.8%, 1154.9%, 1115.6% and 1229% in the Vegetation Area Class (VAC), Agricultural Area Class (AAC), Bare Area Class (BAC) and Salt Lake Class (SLC), respectively. Although the Land Surface Temperature (LST) of all classes has only increased by about 0.4 °C, these changes in radiant energy are much more pronounced. The current study also revealed that most legitimate research on this subject has focused on the effects of global warming on environmental variations. These studies, which see these changes as "results" of climate change and global warming, have overlooked the primary cause, "human demands", which has prompted humans to alter or exploit their surroundings actively. This study found that concentrating on humans and encouraging them to focus on happiness rather than pleasure is more helpful in addressing global warming issues than focusing on its impacts, such as rising sea level, storms, drought, etc. The results of this study are helpful for a deeper understanding of global warming and a careful study of the cause and dimensions of this phenomenon.

Intercropping plays an essential role in enhancing the biodiversity and stability of agroecosystems. The aim of this study was to investigate the optimal pattern and arrangement in safflower/chickpea intercropping in a semi-arid region of Iran. Treatments evaluated in this study were sole cropping of safflower and chickpea, their replacement series (4:4, 2:2, 1:1, 3:1, 1:3), and additive series (20% and 40% of chickpea in both situations in the middle (I) and around (II) of safflower rows). The results showed that the greatest intercropping indices such as land equivalent ratio (LER), area time equivalent ratio (ATER), and system productivity index (SPI) belonged to 40%I additive intercropping pattern. These mentioned values were 1.9, 1.8, and 307.8, respectively. The intercropping patterns had a significant effect on the fatty acid composition of safflower oil. Unsaturated fatty acids including linoleic and oleic acids were higher in the intercropping patterns, whereas, saturated fatty acids consisting of palmitic, myristic, and stearic acids were higher in the safflower sole cropping. Linoleic and oleic acid increased by 9.6% and 16.1% in 40%I compared to sole cropping. Overall, 40%I additive intercropping pattern is more promising in grain and oil yield, intercropping indices, and oil quality than the other intercropping patterns.

This study sought to investigate the influence of chemical additives (AM-P-AA) on the engineering properties of sand dunes, which was polymerized by free radicals in presence of MBA[1] and APS[2], respectively, as a crosslinker and an initiator. Finally, 1 litter per 0.3 m2 of this polymer composite was sprayed at the sample metal trays on 0.5%, 1%, and 2% levels and cured for 30 days to investigate their effects on soil properties. The study was conducted through a completely randomized design with three replications. First, the structure and composition of the stabilizers were determined using Fe-SEM, FTIR, XRD, and swelling capacity. Then, the effect of polymers on the anti-wind erosion ability was examined via a wind tunnel test, compressive strength, abrasion resistance, impact resistance, and crust diameter. The stabilization mechanism refers to a process whereby the sand particles and the polymer are thoroughly bounded to each other.  On the other hand, the improvement of sand properties depended on the stabilizer’s concentration, and the best concentration was found to be 2% (T3), with the sand showing, after 30 days, its highest resistance to any extraneous influence, abrasion, and pressure compared to other treatments. Meanwhile, in a threshold friction velocity experiment performed through a wind tunnel, all samples exhibited resistance to the maximum wind generated (15m/s). Thus, a polymer solution with 2% concentration is highly recommended to effectively stabilize sand dunes.

The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.

Boeoticum specie is a valuable drought-tolerance gene source to breed wheat yield under stress. This study was done to identify the most important traits affecting grain yield of 10 boeoticum ecotypes under drought stress conditions for two years. Water use efficiency, fertile spikes number per plant and seed number per plant showed the highest positive and significant (p≤0.01) correlation with grain yield per plant. Water use efficiency, fertile spikes number per plant, seed number per the main spike, biological yield per plant, and water use (with a negative regression coefficient), as the most important traits, were entered into the regression model, respectively. The most direct effect on increasing grain yield was water use efficiency. Seed number per plant and fertile spikes number per plant, due to increased water use efficiency, showed the most indirect effect on grain yield.  Ecotype 5, as a drought-tolerant ecotype, showed a high water use efficiency by allocating more assimilates to yield components. It had a high grain yield. On the other hand, ecotype 6 was introduced as the most drought-susceptible ecotype with low-economical yield. In this study, high water use efficiency increased the traits related to seed number per plant. The ratio of assimilating allocation to aboveground or under-ground parts was the main mechanism for the adaptation of ecotypes. Therefore, selection based on these mechanisms will lead to the identification of drought-tolerant ecotypes for future wheat breeding programs.

Due to climate change, drought events will probably occur more frequently and be more intense. Therefore, effective drought monitoring and assessment is vital in developing knowledge of drought, drought adaptation, and mitigatory actions. Remote sensing has been widely used for monitoring drought in recent years. In the current research, three groups of remote sensing indices, i.e. vegetation, thermal and moisture indices, were applied to determine the correlation between them and the standardized precipitation index (SPI) as drought index for the growing season (April to September) from 1999 to 2005 for rangeland areas in the Alborz province of Iran. The results indicated that normalized difference vegetation index (NDVI) (with a correlation coefficient of 0.74) and land surface temperature (LST) (with a correlation coefficient of 0.67) had the highest correlations with rainfall. Therefore, it concluded that the assumed indices are suitable for drought monitoring for this land use. Temporal analysis of the results showed that the best correlations of remote sensing indices belonged to the 6- and 9-month SPI and indicated the effect of long-term rainfall on plant growth.